summary.mediate {mediation} | R Documentation |
Function to report results from mediation analysis. Reported categories are mediation effect, direct effect, total effect, and proportion of total effect mediated.
All quantities reported with 95 percent confidence intervals. If the INT = TRUE
option is selected in the mediate function, which allows for a treatment X mediator interaction, then effects are reported separetly for the treatment and control groups.
## S3 method for class 'mediate': summary(object, ...) ## S3 method for class 'summary.mediate': print(x, ...)
object |
Output from mediate function. |
x |
Output from mediate function. |
... |
Additional arguments to be passed. |
Luke Keele, Ohio State University, keele.4@osu.edu , Dustin Tingley, Princeton University, dtingley@princeton.edu, Teppei Yamamoto, Princeton University, tyamamot@princeton.edu, Kosuke Imai, Princeton University, kimai@princeton.edu
Imai, Kosuke, Luke Keele and Dustin Tingley (2009) A General Approach to Causal Mediation Analysis. Imai, Kosuke, Luke Keele and Teppei Yamamoto (2009) Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects.
See also mediate
#Example with JOBS II Field experiment #For illustration purposes simulations set to low number. #Example with JOBS II Field experiment data(jobs) ######################################### #continuous mediator and continuous outcome ######################################### ## Not run: #fit parametric model model.m <- lm(job_seek ~ treat + depress1 + econ_hard + sex + age + occp + marital + nonwhite + educ + income, data=jobs) model.y <- lm(depress2 ~ treat + job_seek + depress1 + econ_hard + sex + age + occp + marital + nonwhite + educ + income, data=jobs) continuous <- mediate(model.m , model.y, sims=1000, T="treat", M="job_seek") summary(continuous) ## End(Not run)